Research on a Bridge Health Monitoring System Based on Machine Learning

This study designs and implements a bridge health monitoring system based on machine learning. The system adopts a four-layer architecture, including data acquisition, processing, analysis and decision-making, and user interaction layers. Core functions encompass data preprocessing, feature extraction, model training, health assessment, and early warning. It integrates algorithms such as Support Vector Machines, Random Forests, and Deep Neural Networks to enhance the accuracy of damage identification and status prediction. The development utilizes modern tools to improve efficiency. Test results show that the system is stable, responsive, and has strong processing capabilities. This system provides an intelligent solution for bridge health monitoring, improving the automation level and management efficiency of monitoring, and offers strong support for bridge safety management.